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First IEEE International Conference on Data Mining (ICDM'01)
Fast Parallel Association Rule Mining without Candidacy Generation
San Jose, California
November 29-December 02
ISBN: 0-7695-1119-8
ASCII Text
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Osmar R. ZaÏane, Mohammad El-Hajj, Paul Lu, "Fast Parallel Association Rule Mining without Candidacy Generation," Data Mining, IEEE International Conference on, pp. 665, First IEEE International Conference on Data Mining (ICDM'01), 2001.
 
 
BibTex
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@article{ 10.1109/ICDM.2001.989600,
author = {Osmar R. ZaÏane and Mohammad El-Hajj and Paul Lu},
title = {Fast Parallel Association Rule Mining without Candidacy Generation},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2001},
isbn = {0-7695-1119-8},
pages = {665},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2001.989600},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining, IEEE International Conference on
TI - Fast Parallel Association Rule Mining without Candidacy Generation
SN - 0-7695-1119-8
SP
EP
A1 - Osmar R. ZaÏane,
A1 - Mohammad El-Hajj,
A1 - Paul Lu,
PY - 2001
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
In this paper we introduce a new parallel algorithm MLFPT (Multiple Local Frequent Pattern Tree) [11] for parallel mining of frequent patterns, based on FP-growth mining, that uses only two full I/O scans of the database, eliminating the need for generating the candidate items and distributing the work fairly among processors. We have devised partitioning strategies at different stages of the mining process to achieve near optimal balancing between processors. We have successfully tested our algorithm on datasets larger than 50 million transactions.
Citation:
Osmar R. ZaÏane, Mohammad El-Hajj, Paul Lu, "Fast Parallel Association Rule Mining without Candidacy Generation," icdm, pp.665, First IEEE International Conference on Data Mining (ICDM'01), 2001
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